Advances in mapping malaria for elimination: fine resolution modelling of Plasmodium falciparum incidence

High-resolution spatio-temporal risk mapping for malaria in Namibia: a comprehensive analysis

BACKGROUND

Namibia, a low malaria transmission country targeting elimination, has made substantial progress in reducing malaria burden through improved case management, widespread indoor residual spraying and distribution of insecticidal nets. The country's diverse landscape includes regions with varying population densities and geographical niches, with the north of the country prone to periodic outbreaks. As Namibia approaches elimination, malaria transmission has clustered into distinct foci, the identification of which is essential for deployment of targeted interventions to attain the southern Africa Elimination Eight Initiative targets by 2030. Geospatial modelling provides an effective mechanism to identify these foci, synthesizing aggregate routinely collected case counts with gridded environmental covariates to downscale case data into high-resolution risk maps.

METHODS

This study introduces innovative infectious disease mapping techniques to generate high-resolution spatio-temporal risk maps for malaria in Namibia. A two-stage approach is employed to create maps using statistical Bayesian modelling to combine environmental covariates, population data, and clinical malaria case counts gathered from the routine surveillance system between 2018 and 2021.

RESULTS

A fine-scale spatial endemicity surface was produced for annual average incidence, followed by a spatio-temporal modelling of seasonal fluctuations in weekly incidence and aggregated further to district level. A seasonal profile was inferred across most districts of the country, where cases rose from late December/early January to a peak around early April and then declined rapidly to a low level from July to December. There was a high degree of spatial heterogeneity in incidence, with much higher rates observed in the northern part and some local epidemic occurrence in specific districts sporadically.

CONCLUSIONS

While the study acknowledges certain limitations, such as population mobility and incomplete clinical case reporting, it underscores the importance of continuously refining geostatistical techniques to provide timely and accurate support for malaria elimination efforts. The high-resolution spatial risk maps presented in this study have been instrumental in guiding the Namibian Ministry of Health and Social Services in prioritizing and targeting malaria prevention efforts. This two-stage spatio-temporal approach offers a valuable tool for identifying hotspots and monitoring malaria risk patterns, ultimately contributing to the achievement of national and sub-national elimination goals.

Managing vector-borne diseases in a geoAI-enabled society. Malaria as an example

More than 17 % of all infectious diseases are caused by vector-borne diseases resulting in more than 1 billion cases and over 1 million deaths each year. Of these malaria continues to be a global burden in over eighty countries. As societies become more digitalised, the availability of geospatially enabled health and disease information will become more abundant. With this, the ability to assess health and disease risks in real-time will become a reality. The purpose of this study was to examine how geographic information, geospatial technologies and spatial data science are being used to reduce the burden of vector-borne diseases such as malaria and explore the opportunities that lie ahead with GeoAI and other geospatial technology advancements. Malaria is a dynamic and complex system and as such a range of data and approaches are needed to tackle different parts of the malaria cycle at different local and global scales. Geospatial technologies provide an integrated framework vital for monitoring, analysing and managing vector-borne diseases. GeoAI and technological advancements are useful for enhancing real-time assessments, accelerating the decision making process and spatial targeting of interventions. Training is needed to enhance the use of geospatial information for the management of vector-borne diseases.

Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania

As malaria transmission declines, the need to monitor the heterogeneity of malaria risk at finer scales becomes critical to guide community-based targeted interventions. Although routine health facility (HF) data can provide epidemiological evidence at high spatial and temporal resolution, its incomplete nature of information can result in lower administrative units without empirical data. To overcome geographic sparsity of data and its representativeness, geo-spatial models can leverage routine information to predict risk in un-represented areas as well as estimate uncertainty of predictions. Here, a Bayesian spatio-temporal model was applied on malaria test positivity rate (TPR) data for the period 2017-2019 to predict risks at the ward level, the lowest decision-making unit in mainland Tanzania. To quantify the associated uncertainty, the probability of malaria TPR exceeding programmatic threshold was estimated. Results showed a marked spatial heterogeneity in malaria TPR across wards. 17.7 million people resided in areas where malaria TPR was high (≥ 30; 90% certainty) in the North-West and South-East parts of Tanzania. Approximately 11.7 million people lived in areas where malaria TPR was very low (< 5%; 90% certainty). HF data can be used to identify different epidemiological strata and guide malaria interventions at micro-planning units in Tanzania. These data, however, are imperfect in many settings in Africa and often require application of geo-spatial modelling techniques for estimation.

Spatio-temporal analysis of malaria incidence and its risk factors in North Namibia

BACKGROUND

Millions of dollars have been spent in fighting malaria in Namibia. However, malaria remains a major public health concern in Namibia, mostly in Kavango West and East, Ohangwena and Zambezi region. The primary goal of this study was to fit a spatio-temporal model that profiles spatial variation in malaria risk areas and investigate possible associations between disease risk and environmental factors at the constituency level in highly risk northern regions of Namibia.

METHODS

Malaria data, climatic data, and population data were merged and Global spatial autocorrelation statistics (Moran's I) was used to detect the spatial autocorrelation of malaria cases while malaria occurrence clusters were identified using local Moran statistics. A hierarchical Bayesian CAR model (Besag, York and Mollie's model "BYM") known to be the best model for modelling the spatial and temporal effects was then fitted to examine climatic factors that might explain spatial/temporal variation of malaria infection in Namibia.

RESULTS

Average rainfall received on an annual basis and maximum temperature were found to have a significant spatial and temporal variation on malaria infection. Every mm increase in annual rainfall in a specific constituency in each year increases annual mean malaria cases by 0.6%, same to average maximum temperature. The posterior means of the time main effect (year t) showed a visible slightly increase in global trend from 2018 to 2020.

CONCLUSION

The study discovered that the spatial temporal model with both random and fixed effects best fit the model, which demonstrated a strong spatial and temporal heterogeneity distribution of malaria cases (spatial pattern) with high risk in most of the Kavango West and East outskirt constituencies, posterior relative risk (RR: 1.57 to 1.78).

Human and vector behaviors determine exposure to Anopheles in Namibia

Background

Although the Republic of Namibia has significantly reduced malaria transmission, regular outbreaks and persistent transmission impede progress towards elimination. Towards an understanding of the protective efficacy, as well as gaps in protection, associated with long-lasting insecticidal nets (LLINs), human and Anopheles behaviors were evaluated in parallel in three malaria endemic regions, Kavango East, Ohangwena and Zambezi, using the Entomological Surveillance Planning Tool to answer the question: where and when are humans being exposed to bites of Anopheles mosquitoes?

Methods

Surveillance activities were conducted during the malaria transmission season in March 2018 for eight consecutive nights. Four sentinel structures per site were selected, and human landing catches and human behavior observations were consented to for a total of 32 collection nights per site. The selected structures were representative of local constructions (with respect to building materials and size) and were at least 100 m from each other. For each house where human landing catches were undertaken, a two-person team collected mosquitoes from 1800 to 0600 hours.

Results

Surveillance revealed the presence of the primary vectors Anopheles arabiensis, Anopheles gambiae sensu stricto (s.s.) and Anopheles funestus s.s., along with secondary vectors (Anopheles coustani sensu lato and Anopheles squamosus), with both indoor and outdoor biting behaviors based on the site. Site-specific human behaviors considerably increased human exposure to vector biting. The interaction between local human behaviors (spatial and temporal presence alongside LLIN use) and vector behaviors (spatial and temporal host seeking), and also species composition, dictated where and when exposure to infectious bites occurred, and showed that exposure was primarily indoors in Kavango East (78.6%) and outdoors in Ohangwena (66.7%) and Zambezi (81.4%). Human behavior-adjusted exposure was significantly different from raw vector biting rate.

Conclusions

Increased LLIN use may significantly increase protection and reduce exposure to malaria, but may not be enough to eliminate the disease, as gaps in protection will remain both indoors (when people are awake and not using LLINs) and outdoors. Alternative interventions are required to address these exposure gaps. Focused and question-based operational entomological surveillance together with human behavioral observations may considerably improve our understanding of transmission dynamics as well as intervention efficacy and gaps in protection.

Graphical Abstract

Spatial and spatio-temporal methods for mapping malaria risk: a systematic review

Background

Approaches in malaria risk mapping continue to advance in scope with the advent of geostatistical techniques spanning both the spatial and temporal domains. A substantive review of the merits of the methods and covariates used to map malaria risk has not been undertaken. Therefore, this review aimed to systematically retrieve, summarise methods and examine covariates that have been used for mapping malaria risk in sub-Saharan Africa (SSA).

Methods

A systematic search of malaria risk mapping studies was conducted using PubMed, EBSCOhost, Web of Science and Scopus databases. The search was restricted to refereed studies published in English from January 1968 to April 2020. To ensure completeness, a manual search through the reference lists of selected studies was also undertaken. Two independent reviewers completed each of the review phases namely: identification of relevant studies based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, data extraction and methodological quality assessment using a validated scoring criterion.

Results

One hundred and seven studies met the inclusion criteria. The median quality score across studies was 12/16 (range: 7–16). Approximately half (44%) of the studies employed variable selection techniques prior to mapping with rainfall and temperature selected in over 50% of the studies. Malaria incidence (47%) and prevalence (35%) were the most commonly mapped outcomes, with Bayesian geostatistical models often (31%) the preferred approach to risk mapping. Additionally, 29% of the studies employed various spatial clustering methods to explore the geographical variation of malaria patterns, with Kulldorf scan statistic being the most common. Model validation was specified in 53 (50%) studies, with partitioning data into training and validation sets being the common approach.

Conclusions

Our review highlights the methodological diversity prominent in malaria risk mapping across SSA. To ensure reproducibility and quality science, best practices and transparent approaches should be adopted when selecting the statistical framework and covariates for malaria risk mapping. Findings underscore the need to periodically assess methods and covariates used in malaria risk mapping; to accommodate changes in data availability, data quality and innovation in statistical methodology.

Closing the gap on institutional delivery in northern India: a case study of how integrated machine learning approaches can enable precision public health

INTRODUCTION

Meeting ambitious global health goals with limited resources requires a precision public health (PxPH) approach. Here we describe how integrating data collection optimisation, traditional analytics and causal artificial intelligence/machine learning (ML) can be used in a use case for increasing hospital deliveries of newborns in Uttar Pradesh, India.

METHODS

Using a systematic behavioural framework we designed a large-scale survey on perceptual, interpersonal and structural drivers of women's behaviour around childbirth (n=5613). Multivariate logistic regression identified factors associated with institutional delivery (ID). Causal ML determined the cause-and-effect ordering of these factors. Variance decomposition was used to parse sources of variation in delivery location, and a supervised learning algorithm was used to distinguish population subgroups.

RESULTS

Among the factors found associated with ID, the causal model showed that having a delivery plan (OR=6.1, 95% CI 6.0 to 6.3), believing the hospital is safer than home (OR=5.4, 95% CI 5.1 to 5.6) and awareness of financial incentives were direct causes of ID (OR=3.4, 95% CI 3.3 to 3.5). Distance to the hospital, borrowing delivery money and the primary decision-maker were not causal. Individual-level factors contributed 69% of variance in delivery location. The segmentation analysis showed four distinct subgroups differentiated by ID risk perception, parity and planning.

CONCLUSION

These findings generate a holistic picture of the drivers and barriers to ID in Uttar Pradesh and suggest distinct intervention points for different women. This demonstrates data optimised to identify key behavioural drivers, coupled with traditional and ML analytics, can help design a PxPH approach that maximise the impact of limited resources.

Mapping the endemicity and seasonality of clinical malaria for intervention targeting in Haiti using routine case data

Towards the goal of malaria elimination on Hispaniola, the National Malaria Control Program of Haiti and its international partner organisations are conducting a campaign of interventions targeted to high-risk communities prioritised through evidence-based planning. Here we present a key piece of this planning: an up-to-date, fine-scale endemicity map and seasonality profile for Haiti informed by monthly case counts from 771 health facilities reporting from across the country throughout the 6-year period from January 2014 to December 2019. To this end, a novel hierarchical Bayesian modelling framework was developed in which a latent, pixel-level incidence surface with spatio-temporal innovations is linked to the observed case data via a flexible catchment sub-model designed to account for the absence of data on case household locations. These maps have focussed the delivery of indoor residual spraying and focal mass drug administration in the Grand’Anse Department in South-Western Haiti.

Satellite Observations and Malaria: New Opportunities for Research and Applications

Satellite remote sensing provides a wealth of information about environmental factors that influence malaria transmission cycles and human populations at risk. Long-term observations facilitate analysis of climate–malaria relationships, and high-resolution data can be used to assess the effects of agriculture, urbanization, deforestation, and water management on malaria. New sources of very-high-resolution satellite imagery and synthetic aperture radar data will increase the precision and frequency of observations. Cloud computing platforms for remote sensing data combined with analysis-ready datasets and high-level data products have made satellite remote sensing more accessible to nonspecialists. Further collaboration between the malaria and remote sensing communities is needed to develop and implement useful geospatial data products that will support global efforts toward malaria control, elimination, and eradication.

Progress towards onchocerciasis elimination in Côte d'Ivoire: A geospatial modelling study

Background

Côte d’Ivoire has had 45 years of intervention for onchocerciasis by vector control (from 1975 to 1991), ivermectin mass drug administration (MDA) (from 1992 to 1994) and community directed treatment with ivermectin (CDTi) from 1995 to the present. We modeled onchocerciasis endemicity during two time periods that correspond to the scale up of vector control and ivermectin distribution, respectively. This analysis illustrates progress towards elimination during these periods, and it has identified potential hotspots areas that are at risk for ongoing transmission.

Methods and findings

The analysis used Ministry of Health skin snip microfilaria (MF) prevalence and intensity data collected between 1975 and 2016. Socio-demographic and environmental factors were incorporated into a predictive, machine learning algorithm to create continuous maps of onchocerciasis endemicity. Overall predicted mean MF prevalence decreased from 51.8% circa 1991 to 3.9% circa 2016. The model predicted infection foci with higher prevalence in the southern region of the country. Predicted mean community MF load (CMFL) decreased from 10.1MF/snip circa 1991 to 0.1MF/snip circa 2016. Again, the model predicts foci with higher Mf densities in the southern region. For assessing model performance, the root mean squared error and R² values were 1.14 and 0.62 respectively for a model trained with data collected prior to 1991, and 1.28 and 0.57 for the model trained with infection survey data collected later, after the introduction of ivermectin. Finally, our models show that proximity to permanent inland bodies of water and altitude were the most informative variables that correlated with onchocerciasis endemicity.

Conclusion/Significance

This study further documents the significant reduction of onchocerciasis infection following widespread use of ivermectin for onchocerciasis control in Côte d’Ivoire. Maps produced predict areas at risk for ongoing infection and transmission. Onchocerciasis might be eliminated in Côte d’Ivoire in the future with a combination of sustained CDTi with high coverage, active surveillance, and close monitoring for persistent infection in previously hyper-endemic areas.

Côte d’Ivoire is endemic for onchocerciasis (also known as “river blindness”). This neglected tropical disease is transmitted by biting black flies that breed in fast flowing rivers. From 1975 to 1991, onchocerciasis control was based on weekly aerial spraying of the insecticide temephos, on black fly breeding sites. Vector control, however, was mostly focused on the northern and central parts of the country. From 1992 to present, mass treatment with ivermectin was implemented in all endemic areas, including forested regions in the south. Here we present the first geospatial estimates of onchocerciasis endemicity over time. Using the machine learning algorithm quantile regression forest, we implemented models to: identify important socio-demographic and environmental factors that correlate with onchocerciasis infection; predict the prevalence and density of infection in areas without ground-truth data; delineate remaining infection hotspots. Our results show that Côte d’Ivoire has made very significant progress in reducing infection parameters over time, and they may help to inform future interventions to achieve the goal of onchocerciasis elimination in Côte d’Ivoire.

Estimating malaria incidence from routine health facility-based surveillance data in Uganda

Background

Accurate measures of malaria incidence are essential to track progress and target high-risk populations. While health management information system (HMIS) data provide counts of malaria cases, quantifying the denominator for incidence using these data is challenging because catchment areas and care-seeking behaviours are not well defined. This study’s aim was to estimate malaria incidence using HMIS data by adjusting the population denominator accounting for travel time to the health facility.

Methods

Outpatient data from two public health facilities in Uganda (Kihihi and Nagongera) over a 3-year period (2011–2014) were used to model the relationship between travel time from patient village of residence (available for each individual) to the facility and the relative probability of attendance using Poisson generalized additive models. Outputs from the model were used to generate a weighted population denominator for each health facility and estimate malaria incidence. Among children aged 6 months to 11 years, monthly HMIS-derived incidence estimates, with and without population denominators weighted by probability of attendance, were compared with gold standard measures of malaria incidence measured in prospective cohorts.

Results

A total of 48,898 outpatient visits were recorded across the two sites over the study period. HMIS incidence correlated with cohort incidence over time at both study sites (correlation in Kihihi = 0.64, p < 0.001; correlation in Nagongera = 0.34, p = 0.045). HMIS incidence measures with denominators unweighted by probability of attendance underestimated cohort incidence aggregated over the 3 years in Kihihi (0.5 cases per person-year (PPY) vs 1.7 cases PPY) and Nagongera (0.3 cases PPY vs 3.0 cases PPY). HMIS incidence measures with denominators weighted by probability of attendance were closer to cohort incidence, but remained underestimates (1.1 cases PPY in Kihihi and 1.4 cases PPY in Nagongera).

Conclusions

Although malaria incidence measured using HMIS underestimated incidence measured in cohorts, even when adjusting for probability of attendance, HMIS surveillance data are a promising and scalable source for tracking relative changes in malaria incidence over time, particularly when the population denominator can be estimated by incorporating information on village of residence.

Global estimation of anti-malarial drug effectiveness for the treatment of uncomplicated Plasmodium falciparum malaria 1991-2019

BACKGROUND

Anti-malarial drugs play a critical role in reducing malaria morbidity and mortality, but their role is mediated by their effectiveness. Effectiveness is defined as the probability that an anti-malarial drug will successfully treat an individual infected with malaria parasites under routine health care delivery system. Anti-malarial drug effectiveness (AmE) is influenced by drug resistance, drug quality, health system quality, and patient adherence to drug use; its influence on malaria burden varies through space and time.

METHODS

This study uses data from 232 efficacy trials comprised of 86,776 infected individuals to estimate the artemisinin-based and non-artemisinin-based AmE for treating falciparum malaria between 1991 and 2019. Bayesian spatiotemporal models were fitted and used to predict effectiveness at the pixel-level (5 km × 5 km). The median and interquartile ranges (IQR) of AmE are presented for all malaria-endemic countries.

RESULTS

The global effectiveness of artemisinin-based drugs was 67.4% (IQR: 33.3-75.8), 70.1% (43.6-76.0) and 71.8% (46.9-76.4) for the 1991-2000, 2006-2010, and 2016-2019 periods, respectively. Countries in central Africa, a few in South America, and in the Asian region faced the challenge of lower effectiveness of artemisinin-based anti-malarials. However, improvements were seen after 2016, leaving only a few hotspots in Southeast Asia where resistance to artemisinin and partner drugs is currently problematic and in the central Africa where socio-demographic challenges limit effectiveness. The use of artemisinin-based combination therapy (ACT) with a competent partner drug and having multiple ACT as first-line treatment choice sustained high levels of effectiveness. High levels of access to healthcare, human resource capacity, education, and proximity to cities were associated with increased effectiveness. Effectiveness of non-artemisinin-based drugs was much lower than that of artemisinin-based with no improvement over time: 52.3% (17.9-74.9) for 1991-2000 and 55.5% (27.1-73.4) for 2011-2015. Overall, AmE for artemisinin-based and non-artemisinin-based drugs were, respectively, 29.6 and 36% below clinical efficacy as measured in anti-malarial drug trials.

CONCLUSIONS

This study provides evidence that health system performance, drug quality and patient adherence influence the effectiveness of anti-malarials used in treating uncomplicated falciparum malaria. These results provide guidance to countries' treatment practises and are critical inputs for malaria prevalence and incidence models used to estimate national level malaria burden.

Sub-national stratification of malaria risk in mainland Tanzania: a simplified assembly of survey and routine data

BACKGROUND

Recent malaria control efforts in mainland Tanzania have led to progressive changes in the prevalence of malaria infection in children, from 18.1% (2008) to 7.3% (2017). As the landscape of malaria transmission changes, a sub-national stratification becomes crucial for optimized cost-effective implementation of interventions. This paper describes the processes, data and outputs of the approach used to produce a simplified, pragmatic malaria risk stratification of 184 councils in mainland Tanzania.

METHODS

Assemblies of annual parasite incidence and fever test positivity rate for the period 2016-2017 as well as confirmed malaria incidence and malaria positivity in pregnant women for the period 2015-2017 were obtained from routine district health information software. In addition, parasite prevalence in school children (PfPR5to16) were obtained from the two latest biennial council representative school malaria parasitaemia surveys, 2014-2015 and 2017. The PfPR5to16 served as a guide to set appropriate cut-offs for the other indicators. For each indicator, the maximum value from the past 3 years was used to allocate councils to one of four risk groups: very low (< 1%PfPR5to16), low (1- < 5%PfPR5to16), moderate (5- < 30%PfPR5to16) and high (≥ 30%PfPR5to16). Scores were assigned to each risk group per indicator per council and the total score was used to determine the overall risk strata of all councils.

RESULTS

Out of 184 councils, 28 were in the very low stratum (12% of the population), 34 in the low stratum (28% of population), 49 in the moderate stratum (23% of population) and 73 in the high stratum (37% of population). Geographically, most of the councils in the low and very low strata were situated in the central corridor running from the north-east to south-west parts of the country, whilst the areas in the moderate to high strata were situated in the north-west and south-east regions.

CONCLUSION

A stratification approach based on multiple routine and survey malaria information was developed. This pragmatic approach can be rapidly reproduced without the use of sophisticated statistical methods, hence, lies within the scope of national malaria programmes across Africa.

Malaria Case Fatality Rate among Children under Five in Burkina Faso: An Assessment of the Spatiotemporal Trends Following the Implementation of Control Programs

Reducing the 2015 level of malaria mortality by 90% by 2030 is a goal set by the World Health Organization (WHO). In Burkina Faso, several malaria control programs proven to be effective were implemented over the last decade. In parallel, the progressive strengthening of the health surveillance system is generating valuable data, which represents a great opportunity for analyzing the trends in malaria burden and assessing the effect of these control programs. Complementary programs were rolled out at different time points and paces, and the present work aims at investigating both the spatial and temporal pattern of malaria case fatality rate (mCFR) by considering the effect of combining specific and unspecific malaria control programs. To this end, data on severe malaria cases and malaria deaths, aggregated at health district level between January 2013 and December 2018, were extracted from the national health data repository (ENDOS-BF). A Bayesian spatiotemporal zero-inflated Poisson model was fitted to quantify the strength of the association of malaria control programs with monthly mCFR trends at health district level. The model was adjusted for contextual variables. We found that monthly mCFR decreased from 2.0 (95% IC 1.9-2.1%) to 0.9 (95% IC 0.8-1.0%) deaths for 100 severe malaria cases in 2013 and 2018, respectively. Health districts with high mCFR were identified in the northern, northwestern and southwestern parts of the country. The availability of malaria rapid diagnosis tests (IRR: 0.54; CrI: 0.47, 0.62) and treatment (IRR: 0.50; CrI: 0.41, 0.61) were significantly associated with a reduction in the mCFR. The risk of dying from malaria was lower in the period after the free healthcare policy compared with the period before (IRR: 0.47; CrI: 0.38, 0.58). Our findings highlighted locations that are most in need of targeted interventions and the necessity to sustain and strengthen the launched health programs to further reduce the malaria deaths in Burkina Faso.

Effect of Free Healthcare Policy for Children under Five Years Old on the Incidence of Reported Malaria Cases in Burkina Faso by Bayesian Modelling: "Not only the Ears but also the Head of the Hippopotamus"

Burkina Faso has recently implemented an additional strategy, the free healthcare policy, to further improve maternal and child health. This policy targets children under five who bear the brunt of the malaria scourge. The effects of the free-of-charge healthcare were previously assessed in women but not in children. The present study aims at filling this gap by assessing the effect of this policy in children under five with a focus on the induced spatial and temporal changes in malaria morbidity. We used a Bayesian spatiotemporal negative binomial model to investigate the space–time variation in malaria incidence in relation to the implementation of the policy. The analysis relied on malaria routine surveillance data extracted from the national health data repository and spanning the period from January 2013 to December 2018. The model was adjusted for meteorological and contextual confounders. We found that the number of presumed and confirmed malaria cases per 1000 children per month increased between 2013 and 2018. We further found that the implementation of the free healthcare policy was significantly associated with a two-fold increase in the number of tested and confirmed malaria cases compared with the period before the policy rollout. This effect was, however, heterogeneous across the health districts. We attributed the rise in malaria incidence following the policy rollout to an increased use of health services combined with an increased availability of rapid tests and a higher compliance to the “test and treat” policy. The observed heterogeneity in the policy effect was attributed to parallel control interventions, some of which were rolled out at different paces and scales. Our findings call for a sustained and reinforced effort to test all suspected cases so that, alongside an improved case treatment, the true picture of the malaria scourge in children under five emerges clearly (see the hippopotamus almost entirely).

An investigation of the Plasmodium falciparum malaria epidemic in Kavango and Zambezi regions of Namibia in 2016

Background

Namibia is one of the countries among the eight that are targeting malaria elimination in southern Africa. However, the country has encountered malaria epidemics in recent years. The objective of this study was to investigate malaria epidemics and to contribute to strengthening malaria surveillance and control in an effort to move Namibia toward eliminating malaria.

Method

Malaria epidemiology data for 2014-2015 were collected from the weekly surveillance system. All consenting household members within a 100-m radius of index households were screened in 2016 using a Carestart malaria HRP2/pLDH combined rapid diagnostic test after epidemics. All houses within this radius were sprayed in 2016 with the pyrethroid deltamethrin and K-Othrine WG 250. Anopheles mosquito-positive breeding sites were identified and treated with the organophosphate larvicide temephos. Insecticide susceptibility and bioassay tests were conducted.

Results

During the epidemic response period in 2016, 56 parasitologically confirmed Plasmodium falciparum malaria cases in the Zambezi region were detected from active screening. The majority of those cases (83%) were asymptomatic infections. In the Kavango region, the malaria epidemic persisted, with 228 P. falciparum malaria cases recorded, but only 97 were investigated. In Namibia, malaria vector susceptibility was detected to 4% dichlorodiphenyltrichloroethane. Indoor residual spraying was conducted in 377 (90%) of the targeted households along with community awareness through health education of 1499 people and distribution of more than 2000 information, education and communication materials. The P. falciparum malaria cases in the Zambezi decreased from 122 in week 9 to 97 after week 15.

Conclusions

Malaria epidemics along with the persistence of asymptomatic reservoir infections pose a serious challenge in Namibia's elimination effort. The country needs to ensure sustainable interventions to target asymptomatic reservoir infections and prevent epidemics in order to successfully achieve its goal of eliminating malaria.

Predicting Risk of Imported Disease with Demographics: Geospatial Analysis of Imported Malaria in Minnesota, 2010-2014

Although immigrants who visit friends and relatives (VFRs) account for most of the travel-acquired malaria cases in the United States, there is limited evidence on community-level risk factors and best practices for prevention appropriate for various VFR groups. Using 2010–2014 malaria case reports, sociodemographic census data, and health services data, we explored and mapped community-level characteristics to understand who is at risk and where imported malaria infections occur in Minnesota. We examined associations with malaria incidence using Poisson and negative binomial regression. Overall, mean incidence was 0.4 cases per 1,000 sub-Saharan African (SSA)–born in communities reporting malaria, with cases concentrated in two areas of Minneapolis–St. Paul. We found moderate and positive associations between imported malaria and counts of SSA- and Asian-born populations, respectively. Our findings may inform future studies to understand the knowledge, attitudes, and practices of VFR travelers and facilitate and focus intervention strategies to reduce imported malaria in the United States.

Malaria Hotspots: Is There Epidemiological Evidence for Fine-Scale Spatial Targeting of Interventions?

As data at progressively granular spatial scales become available, the temptation is to target interventions to areas with higher malaria transmission - so-called hotspots - with the aim of reducing transmission in the wider community. This paper reviews literature to determine if hotspots are an intrinsic feature of malaria epidemiology and whether current evidence supports hotspot-targeted interventions. Hotspots are a consistent feature of malaria transmission at all endemicities. The smallest spatial unit capable of supporting transmission is the household, where peri-domestic transmission occurs. Whilst the value of focusing interventions to high-burden areas is evident, there is currently limited evidence that local-scale hotspots fuel transmission. As boundaries are often uncertain, there is no conclusive evidence that hotspot-targeted interventions accelerate malaria elimination.

Achieving explanatory depth and spatial breadth in infectious disease modelling: Integrating active and passive case surveillance

Ideally, the data used for robust spatial prediction of disease distribution should be both high-resolution and spatially expansive. However, such in-depth and geographically broad data are rarely available in practice. Instead, researchers usually acquire either detailed epidemiological data with high resolution at a small number of active sampling sites, or more broad-ranging but less precise data from passive case surveillance. We propose a novel inferential framework, capable of simultaneously drawing insights from both passive and active data types. We developed a Bayesian latent point process approach, combining active data collection in a limited set of points, where in-depth covariates are measured, with passive case detection, where error-prone, large-scale disease data are accompanied only by coarse or remotely-sensed covariate layers. Using the example of malaria, we tested our method's efficiency under several hypothetical scenarios of reported incidence in different combinations of imperfect detection and spatial complexity of the environmental variables. We provide a simple solution to a widespread problem in spatial epidemiology, combining latent process modelling and spatially autoregressive modelling. By using active sampling and passive case detection in a complementary way, we achieved the best-of-both-worlds, in effect, a formal calibration of spatially extensive, error-prone data by localised, high-quality data.

Using parasite genetic and human mobility data to infer local and cross-border malaria connectivity in Southern Africa

Local and cross-border importation remain major challenges to malaria elimination and are difficult to measure using traditional surveillance data. To address this challenge, we systematically collected parasite genetic data and travel history from thousands of malaria cases across northeastern Namibia and estimated human mobility from mobile phone data. We observed strong fine-scale spatial structure in local parasite populations, providing positive evidence that the majority of cases were due to local transmission. This result was largely consistent with estimates from mobile phone and travel history data. However, genetic data identified more detailed and extensive evidence of parasite connectivity over hundreds of kilometers than the other data, within Namibia and across the Angolan and Zambian borders. Our results provide a framework for incorporating genetic data into malaria surveillance and provide evidence that both strengthening of local interventions and regional coordination are likely necessary to eliminate malaria in this region of Southern Africa.

Predictive analysis across spatial scales links zoonotic malaria to deforestation

The complex transmission ecologies of vector-borne and zoonotic diseases pose challenges to their control, especially in changing landscapes. Human incidence of zoonotic malaria ( Plasmodium knowlesi) is associated with deforestation although mechanisms are unknown. Here, a novel application of a method for predicting disease occurrence that combines machine learning and statistics is used to identify the key spatial scales that define the relationship between zoonotic malaria cases and environmental change. Using data from satellite imagery, a case-control study, and a cross-sectional survey, predictive models of household-level occurrence of P. knowlesi were fitted with 16 variables summarized at 11 spatial scales simultaneously. The method identified a strong and well-defined peak of predictive influence of the proportion of cleared land within 1 km of households on P. knowlesi occurrence. Aspect (1 and 2 km), slope (0.5 km) and canopy regrowth (0.5 km) were important at small scales. By contrast, fragmentation of deforested areas influenced P. knowlesi occurrence probability most strongly at large scales (4 and 5 km). The identification of these spatial scales narrows the field of plausible mechanisms that connect land use change and P. knowlesi, allowing for the refinement of disease occurrence predictions and the design of spatially-targeted interventions.

Spatiotemporal characterisation and risk factor analysis of malaria outbreak in Cabo Verde in 2017

Background

Cabo Verde is a country that has been in the pre-elimination stage of malaria since the year 2000. The country is still reporting cases, particularly in the capital of Praia, where more than 50% of the national population live. This study aims to examine the spatial and temporal epidemiological profile of malaria across the country during the 2017 outbreak and to analyse the risk factors, which may have influenced the trend in malaria cases.

Methods

Longitudinal data collected from all malaria cases in Cabo Verde for the year 2017 were used in this study. The epidemiological characteristics of the cases were analysed. Local and spatial clusters of malaria from Praia were detected by applying the Cluster and Outlier Analysis (Anselin Local Moran's I) to determine the spatial clustering pattern. We then used the Pearson correlation coefficient to analyse the relationship between malaria cases and meteorological variables to identify underlying drivers.

Results

In 2017, 446 cases of malaria were reported in Cabo Verde with the peak of cases in October. These cases were primarily Plasmodium falciparum infections. Of these cases, 423 were indigenous infections recorded in Praia, while 23 were imported malaria cases from different African countries. One case of P. vivax infection was imported from Brazil. Spatial autocorrelation analysis revealed a cluster of high-high malaria cases in the centre of the city. Malaria case occurrence has a very weak correlation (r = 0.16) with breeding site location. Most of the cases (69.9%, R ² = 0.699) were explained by the local environmental condition, with temperature being the primary risk factor followed by relative humidity. A moderately positive relationship was noted with the total pluviometry, while wind speed had a strong negative influence on malaria infections.

Conclusions

In Cabo Verde, malaria remains a serious public health issue, especially in Praia. The high number of cases recorded in 2017 demonstrates the fragility of the situation and the challenges to eliminating indigenous malaria cases and preventing imported cases. Mosquito breeding sites have been the main risk factor, while temperature and precipitation were positively associated with malaria infection. In light of this study, there is an urgent need to reinforce control strategies to achieve the elimination goal in the country.

Mapping influenza activity in emergency departments in France using Bayesian model-based geostatistics

BACKGROUND

Maps of influenza activity are important tools to monitor influenza epidemics and inform policymakers. In France, the availability of a high-quality data set from the Oscour^® surveillance network, covering 92% of hospital emergency department (ED) visits, offers new opportunities for disease mapping. Traditional geostatistical mapping methods such as Kriging ignore underlying population sizes, are not suited to non-Gaussian data and do not account for uncertainty in parameter estimates.

OBJECTIVE

Our objective was to create reliable weekly interpolated maps of influenza activity in the ED setting, to inform Santé publique France (the French national public health agency) and local healthcare authorities.

METHODS

We used Oscour^® data of ED visits covering the 2016-2017 influenza season. We developed a Bayesian model-based geostatistical approach, a class of generalized linear mixed models, with a multivariate normal random field as a spatially autocorrelated random effect. Using R-INLA, we developed an algorithm to create maps of the proportion of influenza-coded cases among all coded visits. We compared our results with maps obtained by Kriging.

RESULTS

Over the study period, 45 565 (0.82%) visits were coded as influenza cases. Maps resulting from the model are presented for each week, displaying the posterior mean of the influenza proportion and its associated uncertainty. Our model performed better than Kriging.

CONCLUSIONS

Our model allows producing smoothed maps where the random noise has been properly removed to reveal the spatial risk surface. The algorithm was incorporated into the national surveillance system to produce maps in real time and could be applied to other diseases.

A cross-sectional analysis of ITN and IRS coverage in Namibia in 2013

Background

Achieving vector control targets is a key step towards malaria elimination. Because of variations in reporting of progress towards vector control targets in 2013, the coverage of these vector control interventions in Namibia was assessed.

Methods

Data on 9846 households, representing 41,314 people, collected in the 2013 nationally-representative Namibia Demographic and Health Survey were used to explore the coverage of two vector control methods: indoor residual spraying (IRS) and insecticide-treated nets (ITNs). Regional data on Plasmodium falciparum parasite rate in those aged 2–10 years (PfPR_2–10), obtained from the Malaria Atlas Project, were used to provide information on malaria transmission intensity. Poisson regression analyses were carried out exploring the relationship between household interventions and PfPR_2–10, with fully adjusted models adjusting for wealth and residence type and accounting for regional and enumeration area clustering. Additionally, the coverage as a function of government intervention zones was explored and models were compared using log-likelihood ratio tests.

Results

Intervention coverage was greatest in the highest transmission areas (PfPR_2–10 ≥ 5%), but was still below target levels of 95% coverage in these regions, with 27.6% of households covered by IRS, 32.3% with an ITN and 49.0% with at least one intervention (ITN and/or IRS). In fully adjusted models, PfPR_2–10 ≥ 5% was strongly associated with IRS (RR 14.54; 95% CI 5.56–38.02; p < 0.001), ITN ownership (RR 5.70; 95% CI 2.84–11.45; p < 0.001) and ITN and/or IRS coverage (RR 5.32; 95% CI 3.09–9.16; p < 0.001).

Conclusions

The prevalence of IRS and ITN interventions in 2013 did not reflect the Namibian government intervention targets. As such, there is a need to include quantitative monitoring of such interventions to reliably inform intervention strategies for malaria elimination in Namibia.

Electronic supplementary material

The online version of this article (10.1186/s12936-018-2417-z) contains supplementary material, which is available to authorized users.

Estimating missing values in China's official socioeconomic statistics using progressive spatiotemporal Bayesian hierarchical modeling

Due to a large number of missing values, both spatially and temporally, China has not published a complete official socioeconomic statistics dataset at the county level, which is the country’s basic scale of official statistics data collection. We developed a procedure to impute the missing values under the Bayesian hierarchical modeling framework. The procedure incorporates two novelties. First, it takes into account spatial autocorrelations and temporal trends for those easier-to-impute variables with small missing percentages. Second, it further uses the first-step complete variables as covariate information to improve the modeling of more-difficult-to-impute variables with large missing percentages. We applied this progressive spatiotemporal (PST) method to China’s official socioeconomic statistics during 2002–2011 and compared it with four other widely used imputation methods, including k-nearest neighbors (kNN), expectation maximum (EM), singular value decomposition (SVD) and random forest (RF). The results show that the PST method outperforms these methods, thus proving the effects of sophisticatedly incorporating the additional spatial and temporal information and progressively utilizing the covariate information. This study has an outcome that allows China to construct a complete socioeconomic dataset and establishes a methodology that can be generally useful for estimating missing values in large spatiotemporal datasets.

Micro-spatial distribution of malaria cases and control strategies at ward level in Gwanda district, Matabeleland South, Zimbabwe

Background

Although there has been a decline in the number of malaria cases in Zimbabwe since 2010, the disease remains the biggest public health threat in the country. Gwanda district, located in Matabeleland South Province of Zimbabwe has progressed to the malaria pre-elimination phase. The aim of this study was to determine the spatial distribution of malaria incidence at ward level for improving the planning and implementation of malaria elimination in the district.

Methods

The Poisson purely spatial model was used to detect malaria clusters and their properties, including relative risk and significance levels at ward level. The geographically weighted Poisson regression (GWPR) model was used to explore the potential role and significance of environmental variables [rainfall, minimum and maximum temperature, altitude, Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), rural/urban] and malaria control strategies [indoor residual spraying (IRS) and long-lasting insecticide-treated nets (LLINs)] on the spatial patterns of malaria incidence at ward level.

Results

Two significant clusters (p < 0.05) of malaria cases were identified: (1) ward 24 south of Gwanda district and (2) ward 9 in the urban municipality, with relative risks of 5.583 and 4.316, respectively. The semiparametric-GWPR model with both local and global variables had higher performance based on AICc (70.882) compared to global regression (74.390) and GWPR which assumed that all variables varied locally (73.364). The semiparametric-GWPR captured the spatially non-stationary relationship between malaria cases and minimum temperature, NDVI, NDWI, and altitude at the ward level. The influence of LLINs, IRS and rural or urban did not vary and remained in the model as global terms. NDWI (positive coefficients) and NDVI (range from negative to positive coefficients) showed significant association with malaria cases in some of the wards. The IRS had a protection effect on malaria incidence as expected.

Conclusions

Malaria incidence is heterogeneous even in low-transmission zones including those in pre-elimination phase. The relationship between malaria cases and NDWI, NDVI, altitude, and minimum temperature may vary at local level. The results of this study can be used in planning and implementation of malaria control strategies at district and ward levels.

Travel patterns and demographic characteristics of malaria cases in Swaziland, 2010-2014

Background

As Swaziland progresses towards national malaria elimination, the importation of parasites into receptive areas becomes increasingly important. Imported infections have the potential to instigate local transmission and sustain local parasite reservoirs.

Methods

Travel histories from Swaziland’s routine surveillance data from January 2010 to June 2014 were extracted and analysed. The travel patterns and demographics of rapid diagnostic test (RDT)-confirmed positive cases identified through passive and reactive case detection (RACD) were analysed and compared to those found to be negative through RACD.

Results

Of 1517 confirmed cases identified through passive surveillance, 67% reported travel history. A large proportion of positive cases reported domestic or international travel history (65%) compared to negative cases (10%). The primary risk factor for malaria infection in Swaziland was shown to be travel, more specifically international travel to Mozambique by 25- to 44-year old males, who spent on average 28 nights away. Maputo City, Inhambane and Gaza districts were the most likely travel destinations in Mozambique, and 96% of RDT-positive international travellers were either Swazi (52%) or Mozambican (44%) nationals, with Swazis being more likely to test negative. All international travellers were unlikely to have a bed net at home or use protection of any type while travelling. Additionally, paths of transmission, important border crossings and means of transport were identified.

Conclusion

Results from this analysis can be used to direct national and well as cross-border targeting of interventions, over space, time and by sub-population. The results also highlight that collaboration between neighbouring countries is needed to tackle the importation of malaria at the regional level.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-017-2004-8) contains supplementary material, which is available to authorized users.

Treatment-seeking behaviour in low- and middle-income countries estimated using a Bayesian model

BACKGROUND

Seeking treatment in formal healthcare for uncomplicated infections is vital to combating disease in low- and middle-income countries (LMICs). Healthcare treatment-seeking behaviour varies within and between communities and is modified by socio-economic, demographic, and physical factors. As a result, it remains a challenge to quantify healthcare treatment-seeking behaviour using a metric that is comparable across communities. Here, we present an application for transforming individual categorical responses (actions related to fever) to a continuous probabilistic estimate of fever treatment for one country in Sub-Saharan Africa (SSA).

METHODS

Using nationally representative household survey data from the 2013 Demographic and Health Survey (DHS) in Namibia, individual-level responses (n = 1138) were linked to theoretical estimates of travel time to the nearest public or private health facility. Bayesian Item Response Theory (IRT) models were fitted via Markov Chain Monte Carlo (MCMC) simulation to estimate parameters related to fever treatment and estimate probability of treatment for children under five years. Different models were implemented to evaluate computational needs and the effect of including predictor variables such as rurality. The mean treatment rates were then estimated at regional level.

RESULTS

Modelling results suggested probability of fever treatment was highest in regions with relatively high incidence of malaria historically. The minimum predicted threshold probability of seeking treatment was 0.3 (model 1: 0.340; 95% CI 0.155-0.597), suggesting that even in populations at large distances from facilities, there was still a 30% chance of an individual seeking treatment for fever. The agreement between correctly predicted probability of treatment at individual level based on a subset of data (n = 247) was high (AUC = 0.978), with a sensitivity of 96.7% and a specificity of 75.3%.

CONCLUSION

We have shown how individual responses in national surveys can be transformed to probabilistic measures comparable at population level. Our analysis of household survey data on fever suggested a 30% baseline threshold for fever treatment in Namibia. However, this threshold level is likely to vary by country or endemicity. Although our focus was on fever treatment, the methodology outlined can be extended to multiple health seeking behaviours captured in routine national survey data and to other infectious diseases.

Exploring the high-resolution mapping of gender-disaggregated development indicators

Improved understanding of geographical variation and inequity in health status, wealth and access to resources within countries is increasingly being recognized as central to meeting development goals. Development and health indicators assessed at national or subnational scale can often conceal important inequities, with the rural poor often least well represented. The ability to target limited resources is fundamental, especially in an international context where funding for health and development comes under pressure. This has recently prompted the exploration of the potential of spatial interpolation methods based on geolocated clusters from national household survey data for the high-resolution mapping of features such as population age structures, vaccination coverage and access to sanitation. It remains unclear, however, how predictable these different factors are across different settings, variables and between demographic groups. Here we test the accuracy of spatial interpolation methods in producing gender-disaggregated high-resolution maps of the rates of literacy, stunting and the use of modern contraceptive methods from a combination of geolocated demographic and health surveys cluster data and geospatial covariates. Bayesian geostatistical and machine learning modelling methods were tested across four low-income countries and varying gridded environmental and socio-economic covariate datasets to build 1×1 km spatial resolution maps with uncertainty estimates. Results show the potential of the approach in producing high-resolution maps of key gender-disaggregated socio-economic indicators, with explained variance through cross-validation being as high as 74-75% for female literacy in Nigeria and Kenya, and in the 50-70% range for many other variables. However, substantial variations by both country and variable were seen, with many variables showing poor mapping accuracies in the range of 2-30% explained variance using both geostatistical and machine learning approaches. The analyses offer a robust basis for the construction of timely maps with levels of detail that support geographically stratified decision-making and the monitoring of progress towards development goals. However, the great variability in results between countries and variables highlights the challenges in applying these interpolation methods universally across multiple countries, and the importance of validation and quantifying uncertainty if this is undertaken.